Wealth Status and Health Insurance Enrollment in India: An Empirical Analysis

Since 2005, health insurance (HI) coverage in India has significantly increased, largely because of the introduction of government-funded pro-poor insurance programs. As a result, the determinants of HI enrollment and their relative importance may have changed. Using National Family Health Survey (NFHS)-4 data, collected in 2015–2016, and employing a Probit regression model, we re-examine the determinants of household HI enrollment. Then, using a multinomial logistic regression model, we estimate the relative risk ratio for enrollment in different HI schemes. In comparison to the results on the determinants of HI enrollment using the NFHS data collected in 2005–2006, we find a decrease in the wealth gap in public HI enrollment. Nonetheless, disparities in enrollment remain, with some changes in those patterns. Households with low assets have lower enrollments in private and community-based health insurance (CBHI) programs. Households with a higher number of dependents have a higher likelihood of HI enrollment, especially in rural areas. In rural areas, poor Scheduled Caste and Scheduled Tribe households are more likely to be enrolled in public HI than the general Caste households. In urban areas, Muslim households have a lower likelihood of enrollment in any HI. The educational attainment of household heads is positively associated with enrollment in private HI, but it is negatively associated with enrollment in public HI. Since 2005–2006, while HI coverage has improved, disparities across social groups remain.


Introduction
India has adopted HI as a healthcare financing tool to achieve Universal Health Coverage (UHC). The growing and diverse HI sector is served by multiple players who provide a variety of HI products. The central government health insurance scheme (CGHS) and employee state insurance scheme (ESIS) cover government and private sector employees, respectively. The community-based health insurance (CBHI) programs, mediated by non-profit organizations, serve poor socioeconomic groups. The Rashtriya Swasthya Bima Yojana (RSBY), a federal program that has been renamed Pradhan Mantri Jan Aarogya Yojana (PM-JAY), and state government HI programs target poor households. India started liberalizing its HI market in 1999 for foreign investments. As a result, private HI has grown [1].
Limited government funding and the cost of HI are among the major impediments to UHC [2]. HI has high demand among poor households because it mitigates the adverse income effect of illness [3,4]. For low-income households, HI is an essential product, and they are willing to pay for it [5]. However, they cannot afford it. As a result, in response to health shocks, they often turn to risky ways to meet healthcare costs [6]. Beyond low data is available for download from the United States Agency for International Development (USAID)'s Demographic and Health Survey (DHS) Program and the International Institute for Population Sciences (IIPS) websites [23,24]. Our study uses data on 21,592 urban and 51,506 rural households across 29 states and seven union territories.

Description of the Variables
Since we closely follow the study by CS, our variables and model specifications are defined accordingly. We utilize information collected in households and eligible women's (women aged between 15 and 49 years) modules of the survey. We briefly describe our outcome and explanatory variables (see Appendix A for additional details).

Health Insurance Enrollment
Our outcome variable of interest is a binary (yes/no) variable, which takes the value of 1 if any usual member of the household is enrolled in any HI scheme at the time of the survey; otherwise, it takes a value of 0. Our sample does not include households with the response "do not know" (approximately 0.60% of the n = 601,509 surveyed households). For households reporting HI enrollment by any of their members (155,531), we define a group variable for HI programs considering their operational mechanism and target population. For instance, we combine ESIS, CGHS, state health insurance schemes, and RSBY as publicly funded HI schemes. The private insurance category includes privately purchased commercial plans and medical reimbursement from the employer [25], while CBHI is considered a distinct insurance choice. The rest of the HI types are grouped under the "other" category. A household not having any HI is categorized as "no-insurance" and serves as our baseline group. Thus, the group variable takes the values: "0 = no-insurance", "1 = public health insurance", "2 = community-based health insurance", "3 = private health insurance", and "4 = Others". We exclude households who responded "more than one health insurance" from our analysis, which accounts for 4% (n = 6461) of the total households who reported having any kind of insurance. However, we check the robustness of our results by including these observations in the "Others" category.

Explanatory Variables
We utilize DHS's household wealth index variable to represent the level of household wealth. In the absence of reliable income and expenditure data, this index is useful in cross-state comparison and evaluating various public health services reaching out to the poorest of the poor [26]. Unlike income data, asset information has fewer miss-reporting chances and does not have seasonal variations [8]. Moreover, it can capture a household's ability to pay recurring insurance premiums.
The explanatory variables include media exposure, dependency variables (no. of household members above 60 years; children 0-5 years and 6-15 years), caste status of households (along with their respective interaction dummies with the asset status), and other control variables (indicators for age, gender, religion of the household head; agriculture and non-agriculture occupation of male and female household members). Media variables capture information barriers that might hinder the uptake of HI [4,25,27]. Dependency variables capture the health risks of the non-working population with higher healthcare needs. Castes capture India's social structure and are relevant for analysis as, historically, SCs, STs, and OBCs have worse socioeconomic and health outcomes than others. The household head's characteristics [28] and the occupation of its members [27] are also known to be linked with the usage of the HI. More recent literature has confirmed the relevance of these variables in predicting the HI enrollment [8,10,13,29]. Among the states, we use Karnataka as the baseline, reference state. Therefore, we include dummies for the remaining states in our empirical analysis. This is also consistent with CS. We group data from Andhra Pradesh and Telangana, which were together as one state in [2005][2006]. Data from union territories have been grouped into one group due to the paucity of data from each union territory. For explanatory variables as well, we exclude households with a response "don't know", which accounts for approximately 1 to 3 percent of the respective sample sizes. Given our large sample, it is reasonable to assume that there is no systematic bias in our estimated result. However, we perform robustness checks by including such observations. No further transformations are conducted on the included variables.

Empirical Strategy
Our unit of analysis is the household eligibility for HI. Since our outcome variable of the interest is a binary variable, and to maintain comparability, we also estimate the model estimated by CS. More specifically, we estimate Probit regression model. Our multivariate Probit regression model can be expressed in the equation form as follows: where Y is HI enrollment, which as defined earlier takes the value of 1 if a household is enrolled in any HI; otherwise, it takes the value of 0. Φ is the cumulative distribution function of the standard normal distribution and X represents a vector of explanatory variables. To estimate RRRs for different HI categories against non-insurance, we estimate MNL model, which is appropriate given that we have more than two insurance products without any natural ordering, and it is also consistent with CS. For this, our equation for the base outcome (no-insurance) is: Pr(y = 0) = 1 1 + e Xβ (1) +e Xβ (2) +e Xβ (3) +e Xβ (4) (2)

Results
In 2015-2016, approximately 29 percent of households had HI, with 28.8% in rural and 28.2% in urban areas [19]. Figure 1 shows the extent of HI enrollment across Indian states. For 2015-2016, we observe a considerable variation in HI enrollment across the states. Andhra Pradesh had the highest number of insured households (74%), followed by Chhattisgarh (69%), Telangana (66%), and Tamil Nadu (64%). In Utter Pradesh, Nagaland, Jammu and Kashmir, and Manipur, less than 10% households were enrolled in any HI scheme. Figure 2 shows the distribution of various HI programs across rural and urban areas. Enrollment in RSBY accounts for the highest share of HI enrollments. The reach of public HI schemes is higher in rural areas, while ESIS, CGHS, and private HI have higher enrollment in urban areas. The enrollment in CBHI schemes in 2005-2006 was 12.07 and 2.73 percent in urban and rural areas, respectively [8]. Table 1   For 2015-2016, we observe a considerable variation in HI enrollment across the states. Andhra Pradesh had the highest number of insured households (74%), followed by Chhattisgarh (69%), Telangana (66%), and Tamil Nadu (64%). In Utter Pradesh, Nagaland, Jammu and Kashmir, and Manipur, less than 10% households were enrolled in any HI scheme. Figure 2 shows the distribution of various HI programs across rural and urban areas. For 2015-2016, we observe a considerable variation in HI enrollment across the states. Andhra Pradesh had the highest number of insured households (74%), followed by Chhattisgarh (69%), Telangana (66%), and Tamil Nadu (64%). In Utter Pradesh, Nagaland, Jammu and Kashmir, and Manipur, less than 10% households were enrolled in any HI scheme. Figure 2 shows the distribution of various HI programs across rural and urban areas. Enrollment in RSBY accounts for the highest share of HI enrollments. The reach of public HI schemes is higher in rural areas, while ESIS, CGHS, and private HI have higher enrollment in urban areas. The enrollment in CBHI schemes in 2005-2006 was 12.07 and 2.73 percent in urban and rural areas, respectively [8]. Table 1 provides a list HI programs and their eligibility criteria. A n d h r a P r a d e s h C h h a t t i s g a r h T e l a n g a n a T a m i l N a d u  Enrollment in RSBY accounts for the highest share of HI enrollments. The reach of public HI schemes is higher in rural areas, while ESIS, CGHS, and private HI have higher enrollment in urban areas. The enrollment in CBHI schemes in 2005-2006 was 12.07 and 2.73 percent in urban and rural areas, respectively [8]. Table 1 provides a list HI programs and their eligibility criteria.  Table 2 shows the distribution of HI enrollment by potential explanatory variables. Except for newspaper, household head's education, and regional dummy, all other variables have a statistically significant association with the HI enrollment. All explanatory variables included in the analysis share statistically significant associations with HI choices. We do not exclude any variables from further analyses due to their theoretical importance and to maintain comparability with CS. In Table 3, we compare our findings with that by CS. Appendix B provides a detailed comparison.

Determinants of HI Enrollment
The marginal effects of wealth status, media, age profile, caste, and other covariates estimated using the Probit model for rural, urban, and combined samples are presented in Table 4.  Delhi excluded from analysis in Rural sample. * in variable names indicates interaction, and # is used as a symbol for the word "number". * p < 0.05, ** p < 0.01, *** p < 0.001.

Role of Household Wealth
Unlike CS's results, we do not find a significant impact of a household's wealth status on its current HI enrollment in rural and urban areas. Compared to 2005-2006, the relative advantage of the wealthier households has decreased across the rural and urban areas. However, after controlling for residence, the highest asset group had a higher probability of having HI than the low wealth group (4 percent higher probability).

Role of Media
Our result is consistent with CS. Among all media variables, CS reports the smallest marginal effects for the radio variable. Similarly, our results are small but insignificant for the variable. The effects of newspaper and television are significant in urban and rural areas, respectively. An urban household with any adult woman reading a newspaper at least once a week had a 2.2 percentage point higher probability of having a member enrolled in HI. Similarly, a rural household with any adult woman watching television at least once a week had a 2.8 percentage point higher probability of having any member enrolled in a HI scheme. Like CS, to isolate the effect of media variables from education and wealth status, we re-estimated our Probit model by including predicted residuals of each of the media variables (results available upon request). The results for media variables remained the same as in our primary model. Thus, the effect of access to media variables persisted after controlling for education and wealth status. Therefore, as CS has noted, insurance providers' better advertising and marketing strategies would help reach yetto-be-insured households, providing them more access to the information on the offered health insurance products.

Role of Dependency Variables
Contrary to CS, we find that in 2015-2016, both in urban and rural areas, households with a higher number of older adults had a higher probability of enrollment in any HI. This suggests that in the era in which RSBY and state-funded programs have been introduced, such households might have enrolled in these programs anticipating greater healthcare need. However, similar to CS, the interaction dummy for high assets and no. of the elderly is insignificant, suggesting no joint effect of high wealth and the high number of older adults on the enrollment.
We find small marginal effects of the number of children in the household. In the urban areas, the probability of HI decreases with the increasing number of children (significant for the 0-5 years group at 2.7 percentage points). In the rural area, we find a small marginal but positive effect (0.5 percent) of the variable representing children aged 6-15 years old. This is consistent with the literature [28,[31][32][33], which found a positive association between age and demand for HI.

Role of Caste
Consistent with CS, in urban areas, we find SC, ST, and OBC (relative to the base category upper caste) are statistically insignificant determinants of HI enrollment. Neither of the lower-caste households with higher assets have significantly different enrollments than the low-asset households from the same castes. However, in rural areas, SC and ST households have a higher likelihood of HI enrollment (5.5 percent and 3.4 percent, respectively). This result indicates that HI enrollments of SC and ST households with lowincome have improved in the rural areas. In contrast to CS, the caste-wealth interaction dummies are statistically insignificant. In the full sample (rural and urban data combined), the SC and OBC households with higher assets have lower likelihoods of HI enrollments.

Role of Other Control Variables
We find a significant but small positive marginal effect of the household head's education on HI enrollment in the urban area. Similar to CS, the likelihood of HI enrollments has not improved for the urban Muslim households compared to other minority religious groups. Hindu households in rural areas have a higher likelihood of HI than minority religious groups' households. The occupational status of male members of the household has no significant effect on HI enrollment. However, the occupational status of female members has significant effects (except for non-agriculture occupation in the urban sample).
CS documented negative coefficients for state dummies, suggesting households living in other states in comparison to Karnataka had lower likelihoods of HI enrollment. In contrast, we find positive coefficients for states such as Andhra Pradesh, Arunachal Pradesh, Chhattisgarh, Kerala, Mizoram, and Tamil Nadu in urban areas (results not shown). In addition, in contrast to CS, we find a significantly lower likelihood of HI enrollment for urban households (4.1 percent), accounting for other factors. The programs like RSBY and state-funded health insurance programs focus on poor households predominantly living in rural areas.

Determinants of HI Enrollment by Schemes
The results from the estimation of MNL are presented in Table 5. As highlighted earlier, with RSBY and state-funded HI programs, public HI became the major category of HI schemes in 2015-2016. Therefore, we present the results for public HI schemes along with CBHI and private HI schemes. We examine the differential impact of household wealth, access to media, demographics, and location on alternative HI enrollment. Due to data limitations, we combine CBHI with "Other" HI in the analysis of the urban area.

Role of Household Wealth
CS documented that households (both low and high caste) with higher asset holdings were more likely to be enrolled in public, CBHI, and private HI schemes, implying enrollment gaps in such schemes between poor and rich households. In contrast, we find this result only for private HI scheme.

Role of Media
Households with access to television at least once a week have a higher likelihood of having public HI in rural areas (RRR 1.2) and private HI in urban areas (RRR 3.39) compared to households without any HI. Reading newspapers has a significant effect on enrollment in private HI vis-à-vis no insurance in the urban areas.

Role of Dependency Variables
Similar to CS, we do not find a significant role of high wealth and high number of older adults on any type of HI enrollment. Additionally, the negative association between the presence of children 0 to 5 years is the same as what CS reported. However, our results for other dependency variables are in contrast to CS. In rural areas, households with members older than 60 years and 6-15 years old have a higher likelihood of enrollment in public HI (RRR 1.08 and 1.04, respectively).

Role of Caste
In urban areas, both low-and high-caste households have a comparable likelihood of being enrolled in public HI. Further, the interaction effect between assets and low-caste variables are insignificant, which is contrary to CS. However, SC and OBC poor households have a higher likelihood of being enrolled in CBHI or private HI when compared with poor upper-caste households. The high-asset SC(OBC) households have a higher probability of enrolling in CBHI and private HI when compared with general-caste and low-asset SC(OBC) households. Contrary to CS's results, the enrollments for rural SC and ST households are higher in public HI (respective RRRs 1.41 and 1.25). Additionally, medium-asset ST (OBC) households are more likely to be enrolled in CBHI than low-asset ST (OBC) households. Overall, we report contrasting results with CS for the interaction dummies of caste and wealth status.

Role of Other Control Variables
Consistent with CS, we find that Muslim households are less likely to be enrolled in any HI compared to non-Muslim households (significant for CBHI enrollment in urban areas). Rural Hindu households are more likely to been enrolled under public HI than the minority religious groups (RRR 1.32). Consistent with CS, we find that higher educational attainment of the household head is positively associated with the likelihood of private HI enrollment. Similarly, we find men's non-agriculture occupation is positively associated with private HI enrollment in urban areas. In the rural areas, except for public HI, member occupations do not significantly affect household enrollment in any HI category. More specifically, women's occupation is positively associated with public HI. The region dummy, which checks urban-rural differences, indicates a higher probability of public HI in rural households. Conversely, urban households have a higher likelihood of private HI enrollment.

Discussion
Our reassessment of the determinants of household HI enrollments show that the roles of household wealth, dependent members, and caste have changed. Poor and lower-caste households are more likely to be enrolled, particularly in public HI programs. Access to media and household head characteristics remain important predictors of HI enrollment. Muslim households continue to have a lower likelihood of enrollment in HI programs. The enrollment momentum shifted from the urban areas in 2005-2006 to the rural areas in 2015-2016. Our results suggest that the increase in HI enrollments can be largely attributed to the introduction of pro-poor public HI programs since 2005.
In contrast to CS, we find that the likelihoods of public HI enrollments of wealthier and poor households are approximately the same. This is likely because the enrollment criteria for RSBY and state-funded HI schemes differ. In some states, the HI schemes allow non-poor households; for example, Andhra Pradesh (including Telangana) and Tamil Nadu use their own list of poor households and cover 80 and 50 percent of their population, respectively [15]. However, high-asset households have a higher likelihood of enrollment in CBHI and private HI.
We find that access to the newspaper and television are significant predictors of HI enrollment. Like CS, households with access to newspapers and television have a higher likelihood of enrollment in private HI. However, in rural areas, households with access to television have a higher likelihood of enrollment in public HI. Thus, CS's finding that access to information influences voluntary HI choices is applicable in both rural and urban areas. We also find that the households with a higher number of dependents have lower likelihoods of enrollment in CBHI and private HI, a finding that is consistent with CS.
In contrast to CS, we find that SC/ST/OBC households with high-asset holding do not have a higher likelihood of enrollment in public HI. However, in rural areas, these households have a lower likelihood of enrollment in public HI. The interaction terms between high assets and low castes are statistically insignificant, which suggests that as far as the likelihood of HI enrollment is concerned, there is no meaningful difference between low-caste households with low and high assets. In urban areas, poor SC and OBC households have a higher likelihood of enrollment in CBHI compared to non-poor SC and OBC households. For private HI, poor lower-caste households, specifically SC (urban) and OBC (urban and rural), have a higher likelihood of enrollment than the poor general-caste households. The enrollment probabilities are higher for poor SC and OBC households. However, on average, high assets are positively associated with enrollment in private HI.
In urban areas, we find that household head's educational attainment is positively associated with enrollment in any HI enrolment, a finding that is in contrast with CS. For private HI enrollment, our finding is consistent with CS and related studies [31][32][33][34][35] as the probability of enrollment increases with increasing years of education. Our education result is contradictory to CS's findings for public HI and CBHI, as fewer years of education are linked with higher odds of enrollment in these programs. The recent literature from India [21] confirms our findings suggesting the changed relationship of this variable. Our results are consistent with the international literature [28]. We find a significantly lower probability of CBHI for urban Muslim households. For the rest of the categories, religious minority households' likelihood of enrollment is statistically indifferent to that of Hindu and Muslim households. India's Muslims have worse education and employment indicators than the other religious groups [36,37]. Despite significant gains in HI enrollments in the last two decades, these programs have yet to sufficiently cover Muslims, who have a greater need for HI. We find that rural households belonging to religious minorities are less likely to have public HI. PM-JAY and state programs need to cover a lot of ground to reach out to these communities. Urban households are more likely to have private HI, while rural households have a higher likelihood of public HI. This result is not surprising, as the private HI providers focus more on urban areas, whereas public HI schemes have a greater reach to households in rural areas.
In the post-RSBY period, we find that the associations between enrollments in HI schemes and some of their explanatory variables have changed, and wealth-based, ethnic, religious, occupational, and geographic disparities in enrollments still exist despite gains in the past 10-15 years. Rising morbidity and mortality, low public health expenditure, out-of-pocket expenditure, and limited coverage of then-existing insurance programs made policymakers think of comprehensive insurance programs [38]. Post 2008, public health insurance coverage increased in India because states like Tamil Nadu, Andhra Pradesh, Karnataka [39], and Maharashtra [17] implemented their own health insurance programs with more generous packages and enrollment criteria. Further, Kerala and Chhattisgarh covered their non-poor households under state-level programs [15,40].
However, increased enrollments do not necessarily translate into improved outcomes for the HI enrollees. RSBY, a major driver of increase, has been studied extensively, and evidence shows that there is some impact on health service utilization but no significant decline in out-of-pocket health expenditure [13,[41][42][43][44][45]. We also detected increased institutional deliveries for the poor, viz non-poor in the post-RSBY period compared to the pre-RSBY period [46]. However, the benefits coverage was limited under the program and was mostly focused on secondary or tertiary healthcare requirements. Despite having several HI programs in the country, a study shows that buying medicine is the most important item in health-related expenditure [47]. Outpatient and medicine costs are mostly paid outof-pocket by the patients. The revamped PM-JAY covers 3 days of pre-hospitalization and 15 days of post-hospitalization expenses on medicines and diagnostics [48]. Still, PM-JAY beneficiaries have out-of-pocket expenditure [49]. A study suggests that the program is ineffective in reducing catastrophic health expenditure [50].
Rising health inequities in India is another concern that hinders the progress toward UHC. A recent study finds that India is behind on 19 out of 33 Sustainable Development Goal (SDG) targets [51]. Seventy-five percent of districts in India are well behind the target on these indicators. Public health subsidy for treating chronic diseases in hospitals is largely utilized by the rich [52]. The life expectancy gap between the poorest and richest households is 7.6 years [53]. Moreover, geographic, ethnic, religious, and gendered health disparities are rising [54]. Caste is an important dimension to health disparities in India. Lower caste households have the worst nutritional status [55,56], life expectancy [57], infant mortality [58], and other important healthcare indicators.
Therefore, to address these inequities, public health infrastructure needs to be improved. Despite a significant gain over the past decade, the neglect of public institutions and diverting of resources to private care via health insurance programs have adversely affected the public health infrastructure. Studies suggest that such programs encourage profiteering behavior by the private health sector [13,43]. Strengthening public health infrastructure is a potentially more economical and effective option [13,43,59]. Moreover, health disparity in India ought to be examined from a socioecological framework [60] because enrollment in HI does not necessarily translate into program acceptability [61].
The coverage of CBHI declined between 2005 and 2006 and 2015 and 2016. We exclude households who reported more than one HI and "don't know" from our main analysis. However, we perform robustness checks by re-estimating MNL results after including households with "more than one HI" in the Other HI category and estimation of Probit and MNL models by including households with "don't know". We find that the results do not differ qualitatively from our main analysis (see Appendices C-E).
Our analysis has a few limitations. To maintain the comparability with CS, we do not distinguish the RSBY and state-funded programs from the employee-targeted CBHI and ESIS within the public HI programs. Additionally, we do not analyze the interstate variability in HI enrollment due to the desire to compare our findings with that of CS. States play an important role in the implementation of public HI programs. In recent years, some states have expanded eligibility criteria, covering almost their entire populations (see Table 1). Moreover, states are implementing PM-JAY by adopting either trust, insurance, or mixed modes of program implementation, causing variations in their program administration [48], which may have affected HI enrollments. For future research, given the recent changes in the HI sector, analyzing HI enrollment by states, ethnicity, and income will be insightful.

Conclusions
India introduced RSBY in 2008 to provide health insurance to poor households. In addition, similar state-level health insurance programs were adopted by various states. Therefore, it is reasonable to believe that the last 15 years have been more favorable to the poor as far as access to health insurance is concerned, which is also reflected in improved enrollments of poor households in different health insurance schemes. In a comprehensive study, CS explored the determinants of HI enrollment in India using data from the NFHS that was collected in 2005-206. In light of the introduction of RSBY and state-level HI schemes, it is expected that the relative roles and significance of the determinants of household enrollment in HI may have changed.
In this paper, following CS and using NFHS data that was collected in 2015-2016, we re-examine the determinants of household HI enrollment. In contrast to CS, we find that households with high assets are as likely to be enrolled in any HI as the households with low assets. Lower-caste households, especially in rural areas, have a higher likelihood of HI enrollment. Households with a higher number of dependents (i.e., elderly and children) are more likely to be enrolled in any HI. In addition, urban households are less likely to be enrolled in HI compared to rural households. On the other hand, consistent with CS, Muslim households are less likely to be enrolled in any HI compared to non-Muslim households. The educational attainment and age of the household head and women's occupations are positively associated with enrollment in HI. Regarding enrollment in different HI programs, contrary to CS, we find that households with higher assets are as likely to be enrolled in public HI as households with low assets. Households with a higher number of dependents have a higher likelihood of enrollment in public HI. The coverage momentum has shifted to the rural areas in 2015-2016 from the urban areas in 2005-2006. While there has been a significant gain in HI enrollments, disparities across socioeconomic groups remain.

Institutional Review Board Statement:
We used a secondary dataset collected by International Institute for Population Sciences (IIPS) and the Demographic and Health Survey (DHS). We received approval from the DHS program to utilize the NFHS-4 dataset for this study. The University of Arizona Institutional Review Board gave additional approval to conduct the study (protocol number 774 1811113846).

Data Availability Statement:
The data presented in this study are available upon request from the Demographic and Health Surveys (DHS) website.
Acknowledgments: This study was completed as a part of doctoral dissertation work for PA. We thank Elizabeth Calhoun, Smita Pakhale, and Matthew Butler for their helpful comments and suggestions. Usual disclaimers apply.

Conflicts of Interest:
The authors declare no conflict of interest. Table A1. Definitions of the Variables.

Variable Abbreviation Definition
HI Dummy variable for any usual member in the household has health insurance (0 = not enrolled, 1 = enrolled in HI).

Variable Abbreviation Definition
Newspaper Dummy variable for reading newspaper (0 = does not read newspaper, 1 = read newspaper)

Radio
Dummy variable for listening to radio (0 = does not listen radio, 1 = listen radio) Appendix B Table A2. Detailed Comparative Summary.

A. Determinants of Health Insurance Enrollment
Chakravarti and Shankar [15] Our Study Table A2. Cont.

B. Determinants of Health Insurance Choices
Outcome 1: Public HI Chakravarti and Shankar [15] Our Study Table A2. Cont.